ORIGINAL_ARTICLE
Cover vol. 14, no. 5, October 2017
http://ijfs.usb.ac.ir/article_3438_c321127867045c0b7691270c858d873b.pdf
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10.22111/ijfs.2017.3438
ORIGINAL_ARTICLE
A NOVEL FUZZY-BASED SIMILARITY MEASURE FOR COLLABORATIVE FILTERING TO ALLEVIATE THE SPARSITY PROBLEM
Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based on fuzzy set theory along with default voting technique aimed to provide a valid similarity measurement between users wherever the available ratings are relatively rare. The main idea of this research is to model the rating behaviour of each user by a fuzzy set, and use this model to determine the user's degree of interest on items. Experimental results on the MovieLens and Netflix datasets show the effectiveness of the proposed algorithm in handling data sparsity problem. It also outperforms some state-of-the-art collaborative filtering algorithms in terms of prediction quality.
http://ijfs.usb.ac.ir/article_3429_9dd80fbae48d72d9c3e7414611160480.pdf
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10.22111/ijfs.2017.3429
Recommender system
Collaborative filtering
Similarity measure
Data sparsity
Masoud
Saeed
msaeedmz@gmail.com
true
1
School of Electrical and Computer Engineering, Shiraz University,
Shiraz, Iran
School of Electrical and Computer Engineering, Shiraz University,
Shiraz, Iran
School of Electrical and Computer Engineering, Shiraz University,
Shiraz, Iran
LEAD_AUTHOR
Eghbal G
Mansoori
true
2
School of Electrical and Computer Engineering, Shiraz University,
Shiraz, Iran
School of Electrical and Computer Engineering, Shiraz University,
Shiraz, Iran
School of Electrical and Computer Engineering, Shiraz University,
Shiraz, Iran
AUTHOR
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Applications, 38(5) (2011), 5101{5109.
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Based Systems, 46 (2013), 109{132.
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User Modeling and User-Adapted Interaction, 25(2) (2015), 99{154.
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In Proceedings of the 2nd Indian International Conference on Articial Intelligence, 5 (2005), 2231{
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recommendation, In Pacic Rim International Conference on Articial Intelligence, (2010), 39{51.
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methods, In Recommender Systems Handbook, (2011), 107{144.
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between nodes of a graph with application to collaborative recommendation, IEEE Transactions on
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Knowledge and Data Engineering, 19(3) (2007), 355{369.
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ltering algorithm, Information Retrieval, 4(2) (2001), 133{151.
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recommender systems, ACM Transactions on Information Systems (TOIS), 22(1) (2004), 5{53.
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[21] G. Koutrika, B. Bercovitz and H. Garcia-Molina, FlexRecs: expressing and combining
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recommendations, In Proceedings of the 2009 ACM SIGMOD International Conference on
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Management of Data, (2009), 745{758.
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association rules and multiple-level similarity, Knowledge and Information Systems, 10(3) (2006),
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user similarity and global user similarity, Machine Learning, 72(3) (2008), 231{245.
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In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and
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Handbook, Springer US, (2011), 257{297.
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[34] U. Shardanand and P. Maes, Social information ltering: Algorithms for automating word of
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mouth, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, (1995),
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[35] Y. Shi, M. Larson and A. Hanjalic, Collaborative ltering beyond the user-item matrix: A survey
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of the state of the art and future challenges, ACM Computing Surveys (CSUR), 47(1) (2014), 3.
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Intelligence, (2009), 2{19.
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[37] R. Yera, J. Castro and L. Martnez, A fuzzy model for managing natural noise in recommender
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systems, Applied Soft Computing, 40 (2016), 187{198.
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Congress on, (2014), 5{7.
86
ORIGINAL_ARTICLE
DISTINGUISHABILITY AND COMPLETENESS OF CRISP DETERMINISTIC FUZZY AUTOMATA
In this paper, we introduce and study notions like state-\\linebreak distinguishability, input-distinguishability and output completeness of states of a crisp deterministic fuzzy automaton. We show that for each crisp deterministic fuzzy automaton there corresponds a unique (up to isomorphism), equivalent distinguished crisp deterministic fuzzy automaton. Finally, we introduce two axioms related to output completeness of states and discuss the interrelationship between them.
http://ijfs.usb.ac.ir/article_3430_47258f258c4b3466bedcf1c4d7d39c8a.pdf
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30
10.22111/ijfs.2017.3430
Crisp deterministic fuzzy automaton
Indistinguishable states
Input-indistinguishable
Homomorphism
Output complete
Renu
.
renuismmaths@gmail.com
true
1
Indian School of Mines, Dhanbad, India
Indian School of Mines, Dhanbad, India
Indian School of Mines, Dhanbad, India
LEAD_AUTHOR
S. P.
Tiwari
true
2
Department of Applied Mathematics, Indian Institute of Technology
(ISM), Dhanbad-826004, India
Department of Applied Mathematics, Indian Institute of Technology
(ISM), Dhanbad-826004, India
Department of Applied Mathematics, Indian Institute of Technology
(ISM), Dhanbad-826004, India
AUTHOR
[1] Z. Bavel, Structure and transition preserving functions of nite automata, Journal of Asso-
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ciation for Computing machinery, 15 (1968), 135{158.
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[2] Y. Cao and Y. Ezawa, Nondeterministic fuzzy automata, Information Sciences, 191 (2012),
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[3] M. Ciric and J. Ignjatovic, Fuzziness in automata theory: why? how?, Studies in Fuzziness
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and Soft Computing, 298 (2013), 109{114.
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[4] M. Doostfatemeh and S. C. Kremer, New directions in fuzzy automata, International Journal
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of Approximate Reasoning, 38 (2005), 175{214.
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[5] S. Ginsburg, Some remark on abstract automata, Transactions of the American Mathematical
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Society, 96 (1960), 400{444.
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[6] X. Guo, Grammar theory based on lattice-order monoid, Fuzzy Sets and Systems, 160 (2009),
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1152{1161.
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[7] W. M. L. Holcombe, Algebraic automata theory, Cambridge University Press, Cambridge,
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[8] J. Ignjatovic, M. Ciric and S. Bogdanovic, Determinization of fuzzy automata with member-
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ship values in complete residuated lattices, Information Sciences, 178 (2008), 164{180.
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[9] J. Ignjatovic, M. Ciric, S. Bogdanovic and T. Petkovic, Myhill-Nerode type theory for fuzzy
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languages and automata, Fuzzy Sets and Systems, 161 (2010), 1288{1324.
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[10] M. Ito, Algebraic structures of automata, Theoretical Computer Science, 429 (2012), 164{
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[11] J. Jin, Q. Li and Y. Li, Algebraic properties of L-fuzzy nite automata, Information Sciences,
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234 (2013), 182{202.
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[12] Y. B. Jun, Intuitionistic fuzzy nite state automata, Journal of Applied Mathematics and
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Computing, 17 (2005), 109{120.
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[13] Y. B. Jun, Intuitionistic fuzzy nite switchboard state automata, Journal of Applied Mathe-
22
matics and Computing, 20 (2006), 315{325.
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[14] Y. B. Jun, Quotient structures of intuitionistic fuzzy nite state automata, Information Sci-
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ences, 177 (2007), 4977{4986.
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[15] Y. H. Kim, J. G. Kim and S. J. Cho, Products of T-generalized state automata and T-
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generalized transformation semigroups, Fuzzy Sets and Systems, 93 (1998), 87{97.
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[16] H. V. Kumbhojkar and S. R. Chaudhari, On proper fuzzication of fuzzy nite state automata,
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International Journal of Fuzzy Mathematics, 4 (2008), 1019{1027.
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[17] Y. Li and W. Pedrycz, Fuzzy nite automata and fuzzy regular expressions with membership
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values in lattice-ordered monoids, Fuzzy Sets and Systems, 156 (2005), 68{92.
31
[18] Y. Li and W. Pedrycz, The equivalence between fuzzy Mealy and fuzzy Moore automata, Soft
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Computing, 10 (2006), 953{959.
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[19] Y. Li and Q. Wang, The universal fuzzy automaton, Fuzzy Sets and Systems, 249 (2014),
34
[20] D. S. Malik, J. N. Mordeson and M. K. Sen, Subautomata of fuzzy nite state automaton,
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Journal of Fuzzy Mathematics, 2 (1994), 781{792.
36
[21] J. N. Mordeson and D. S. Malik, Fuzzy automata and languages: Theory and Applications,
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Chapman and Hall/CRC. London/Boca Raton, 2002.
38
[22] K. Peeva and Zl. Zahariev, Computing behavior of nite fuzzy automata-algorithm and its
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application to reduction and minimization, Information Sciences, 178 (2008), 4152{4165.
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[23] D. Qiu, Automata theory based on complete residuated lattice-valued logic (I), Science in
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China, 44 (2001), 419{429.
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[24] D. Qiu, Automata theory based on complete residuated lattice-valued logic (II), Science in
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China, 45 (2002), 442{452.
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[25] E. S. Santos, General formulation of sequential automata, Information and control, 12 (1968),
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[26] W. Shukla and A. K. Srivastava, A topology for automata: A note, Information and Control,
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32 (1976), 163{168.
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[27] A. K. Srivastava and W. Shukla, A topology for automata II, International Journal of Math-
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ematics and Mathematical Sciences, 9 (1986), 425{428.
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[28] S. P. Tiwari and S. Sharan, Fuzzy automata based on lattice-ordered monoid with algebraic
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and topological aspects, Fuzzy Information and Engineering, 4 (2012), 155{164.
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[29] S. P. Tiwari and A. K. Singh, On minimal realization of fuzzy behavior and associated cate-
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gories, Journal of Applied Mathematics and Computing, 45 (2014), 223{234.
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[30] S. P. Tiwari, A. K. Singh and S. Sharan, Fuzzy subsystems of fuzzy automata based on lattice
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ordered monoid, Annals of Fuzzy Mathematics and Informatics, 7 (2013), 437{445.
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Journal of Fuzzy Systems, 12 (2015), 63{73.
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[32] D. Todinca and D. Butoianu, VHDL framework for modeling fuzzy automata, in: Proc. 14th
58
International Symposium on Symbolic and Numeric Algorithms for Scientic Computing,
59
IEEE, (2012), 171{178.
60
ORIGINAL_ARTICLE
BATHTUB HAZARD RATE DISTRIBUTIONS AND FUZZY LIFE TIMES
The development of life time analysis started back in the $20^{\textit{th}}$ century and since then comprehensive developments have been made to model life time data efficiently. Recent development in measurements shows that all continuous measurements can not be measured as precise numbers but they are more or less fuzzy. Life time is also a continuous phenomenon, and has already been shown that life time observations are not precise measurements but fuzzy. Therefore, the corresponding analysis techniques employed on the data require to consider fuzziness of the observations to obtain appropriate estimates.In this study generalized estimators for the parameters and hazard rates are proposed for bathtub failure rate distributions to model fuzzy life time data effectively.
http://ijfs.usb.ac.ir/article_3431_9cc7cc95245cf4eede6534e9ece4735e.pdf
2017-10-29T11:23:20
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31
41
10.22111/ijfs.2017.3431
Bathtub failure rate
Fuzzy number
Life time
Non-precise data
Muhammad
Shafiq
shafiq@kust.edu.pk
true
1
Institute of Statistics and Mathematical Methods in Economics,
Vienna University of Technology, Wien, Austria
Institute of Statistics and Mathematical Methods in Economics,
Vienna University of Technology, Wien, Austria
Institute of Statistics and Mathematical Methods in Economics,
Vienna University of Technology, Wien, Austria
LEAD_AUTHOR
Reinhard
Viertl
r.viertl@tuwien.ac.at
true
2
Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Wien, Austria
Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Wien, Austria
Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Wien, Austria
AUTHOR
[1] P. Abbas, P. G. Ali and S. Mansour, Reliability estimation in Rayleigh distribution based on
1
fuzzy lifetime data, International Journal of System Assurance Engineering and Management,
2
5(5) (2013), 487{494.
3
[2] G. Barbato, A. Germak, G. Genta and A. Barbato, Measurements for Decision Making.
4
Measurements and Basic Statistics, Esculapio, Bologna, 2013.
5
[3] M. R. Casals, A. Colubi, N. Corral, M. A. Gil, M. Montenegro, M. A. Lubiano, A. B. Ramos-
6
Guajardo, B. Sinova and others, Random fuzzy sets: a mathematical tool to develop statistical
7
fuzzy data analysis, Iranian Journal of Fuzzy Systems, 10(2) (2013), 1{28.
8
[4] Z. Chen, A new two-parameter lifetime distribution with bathtub shape or increasing failure
9
rate function, Statistics & Probability Letters, 49(2) (2000), 155{161.
10
[5] V. Couallier, L. Gerville-Reache, C. Huber-Carol, N. Limnios and M. Mesbah, Statistical
11
Models and Methods for Reliability and Survival Analysis, Wiley, London, 2013.
12
[6] M. S. Hamada, A. Wilson, C. S. Reese and H. Martz, Bayesian Reliability, Springer, New
13
York, 2008.
14
[7] E. Haupt and H. Schabe, A new model for a lifetime distribution with bathtub shaped failure
15
rate, Microelectronics Reliability, 32(5) (1992), 633{639.
16
[8] D. W. Hosmer and S. Lemeshow, Applied Survival Analysis: Regression Modeling of Time
17
to Event Data, Wiley, New York, 1999.
18
[9] H. Huang, M. J. Zuo and Z. Sun, Bayesian reliability analysis for fuzzy lifetime data, Fuzzy
19
Sets and Systems, 157(12) (2006), 1674{1686.
20
[10] N. Hung T and W. Berlin, Fundamentals of Statistics with Fuzzy Data, Springer, New York,
21
[11] J. G. Ibrahim, M. H. Chen and D. Sinha, Bayesian Survival Analysis, Springer, New York,
22
[12] D. G. Kleinbaum and M. Klein, Survival Analysis: A self-learning Text, Springer, New York,
23
[13] G. J. Klir and Y. Bo, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice - Hal,
24
New Jersey, 1995.
25
[14] K. H. Lee, First Course on Fuzzy Theory and Applications, Springer, London, 2006.
26
[15] E. T. Lee and J. W.Wang, Statistical Methods for Survival Data Analysis, Wiley, New Jersey,
27
[16] W. Q. Meeker and L. A. Escobar, Statistical Methods for Reliability Data, Wiley, New York,
28
[17] R. G. Miller, Survival Analysis, Wiley, New York, 2011.
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[18] W. B. Nelson, Applied Life Data Analysis, Wiley, New Jersey, 2005.
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[19] S. G. Tzafestas and A. N. Venetsanopoulos, Fuzzy Reasoning in Information, Decision, and
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Control Systems, Kluwer Academic Publishers, Norwell, MA, 1994.
32
[20] R. Viertl, Beschreibung Und Analyse Unscharfer Information: Statistische Methoden Fur
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unscharfe Daten, Springer, Wien, 2006.
34
[21] R. Viertl, On reliability estimation based on fuzzy lifetime data, Journal of Statistical Planning
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& Inference, 139(5) (2009), 1750{1755.
36
[22] R. Viertl, Statistical Methods for Fuzzy Data, Wiley, Chichester, 2011.
37
[23] R. Viertl, Univariate statistical analysis with fuzzy data, Computational Statistics & Data
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Anaysis, 51(1) (2006), 133{147.
39
[24] H. C. Wu, Fuzzy bayesian estimation on lifetime data, Computational Statistics, 19(4)
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(2004), 613-633.
41
[25] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338{353.
42
[26] H. J. Zimmermann, Fuzzy Set Theory - and Its Applications, Kluwer Academic Publishers,
43
Massachusetts, 2001.
44
ORIGINAL_ARTICLE
ADAPTIVE FUZZY OUTPUT FEEDBACK TRACKING CONTROL FOR A CLASS OF NONLINEAR TIME-VARYING DELAY SYSTEMS WITH UNKNOWN BACKLASH-LIKE HYSTERESIS
This paper considers the problem of adaptive output feedback tracking control for a class of nonstrict-feedback nonlinear systems with unknown time-varying delays and unknown backlash-like hysteresis. Fuzzy logic systems are used to estimate the unknown nonlinear functions. Based on the Lyapunov–Krasovskii method, the control scheme is constructed by using the backstepping and adaptive technique. The proposed adaptive controller guarantees that all the closed-loop signals are semiglobally uniformly ultimately bounded and the tracking error can converge to a small neighborhood of the origin. Finally, Simulation results further show the effectiveness of the proposed approach.
http://ijfs.usb.ac.ir/article_3432_cc814b436b91bfe3ef40ebdb32c04112.pdf
2017-10-01T11:23:20
2018-02-25T11:23:20
43
64
10.22111/ijfs.2017.3432
Adaptive fuzzy control
Backstepping design technique
Backlash-like hysteresis
Nonstrict-feedback form
Nonlinear control
Mohsen
Hasanpour Naseriyeh
mohsen.hasanpour@eng.uk.ac.ir
true
1
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
AUTHOR
Adeleh
Arabzadeh Jafari
aarabzadeh@eng.uk.ac.ir
true
2
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
AUTHOR
Seyed Mohammad Ali
Mohammadi
a_mohammadi@uk.ac.ir
true
3
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
LEAD_AUTHOR
[1] B. Chen, C. Lin, X. Liu and K. Liu, Observer-based adaptive fuzzy control for a class of
1
nonlinear delayed systems, IEEE Trans. Syst., Man, Cybern. Syst., 46(1) (2016), 27{36.
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[2] B. Chen, X. Liu and K. Liu and C. Lin, Direct adaptive fuzzy control of nonlinear strict-
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feedback systems, Automatica, 45(6) (2009), 1530{1535.
4
[3] B. Chen, X. P. Liu, S. S. Ge and C. Lin, Adaptive fuzzy control of a class of nonlinear systems
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by fuzzy approximation approach, IEEE Trans. Fuzzy Syst, 20(6) (2012), 1012{1021.
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[4] S. S. Ge, F. Hong and T. H. Lee, Adaptive neural network control of nonlinear systems with
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unknown time delays, IEEE Trans. Autom. Control, 48(11) (2003), 2004{2010.
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[5] F. Gmez Estern, A. Barreiro, J. Aracil and F. Gordillo, Robust generation of almost-periodic
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oscillations in a class of nonlinear systems, Int. J. Robust and Control, 16(18) (2006),
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[6] C. Hua and X. Guan, Output feedback stabilization for time-delay nonlinear interconnected
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systems using neural networks, IEEE Trans. Neural Netw., 19(4) (2008), 673{688.
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[7] L. R. Hunt and G. Meyer, Stable inversion for nonlinear systems, Automatica, 33(8) (1997),
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1549{1554.
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[8] M. Krstic, I. Kanellakopoulos and P. V. Kokotovic, Nonlinear and Adaptive Control Design,
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New York, NY, USA: Wiley, 1995.
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[9] H. Lee and M. Tomizuka, Robust adaptive control using a universal approximation for SISO
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nonlinear systems, IEEE Trans. Fuzzy Syst., 8(1) (2001), 95{106.
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[10] Y. Li and S. C. Tong, Adaptive fuzzy output-feedback stabilization control for a class of
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switched nonstrict-feedback nonlinear systems, IEEE Trans. Cybern., 99 (2016), 1{10.
20
[11] Y. Li, S. C. Tong, Y. Liu and T. Li, Adaptive fuzzy robust output feedback control of nonlinear
21
systems with unknown dead zones based on a small-gain approach, IEEE Trans. Fuzzy Syst,
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22(1) (2014), 164-176.
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[12] Z. Liu, G. Lai, Y. Zhang, X. Chen and C. L. P. Chen, Adaptive neural control for a class of
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nonlinear time-varying delay systems with unknown hysteresis, IEEE Trans. Neural Netw.
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Learn. Syst, 25(12) (2014), 2129{2140.
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[13] A. E. Ougli and B. Tidhaf, Optimal type-2 fuzzy adaptive control for a class of uncertain
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nonlinear systems using an LMI approach, Int. J. Innov. Comput. I., 11(3) (2015), 851-863.
28
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of nonlinear uncertain systems, IEEE Trans. Cybern, 46(6) (2016), 1476{1483.
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systems by fuzzy approximation approach, IEEE Trans. Fuzzy Syst, 21(2) (2013), 301{313.
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linear systems with unknown backlash-like hysteresis, IEEE Trans.Autom. Control, 49(10)
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71
ORIGINAL_ARTICLE
K-FLAT PROJECTIVE FUZZY QUANTALES
In this paper, we introduce the notion of {\bf K}-flat projective fuzzy quantales, and give an elementary characterization in terms of a fuzzy binary relation on the fuzzy quantale. Moreover, we prove that {\bf K}-flat projective fuzzy quantales are precisely the coalgebras for a certain comonad on the category of fuzzy quantales. Finally, we present two special cases of {\bf K} as examples.
http://ijfs.usb.ac.ir/article_3433_72d18bb9c00190de1a6790f1395bafc0.pdf
2017-10-29T11:23:20
2018-02-25T11:23:20
65
81
10.22111/ijfs.2017.3433
Fuzzy quantale
Fuzzy binary relation
{bf K}-flat projective fuzzy quantale
Comonad
Jing
Lu
1044250817@qq.com
true
1
College of Mathematics and Information Science, Shaanxi Normal Univer-
sity, Xi'an 710119, P.R. China
College of Mathematics and Information Science, Shaanxi Normal Univer-
sity, Xi'an 710119, P.R. China
College of Mathematics and Information Science, Shaanxi Normal Univer-
sity, Xi'an 710119, P.R. China
AUTHOR
Kaiyun
Wang
wangkaiyun@snnu.edu.cn
true
2
College of Mathematics and Information Science, Shaanxi Normal
University, Xi'an 710119, P.R. China
College of Mathematics and Information Science, Shaanxi Normal
University, Xi'an 710119, P.R. China
College of Mathematics and Information Science, Shaanxi Normal
University, Xi'an 710119, P.R. China
AUTHOR
Bin
Zhao
zhaobin@snnu.edu.cn
true
3
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, P.R. China
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, P.R. China
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, P.R. China
LEAD_AUTHOR
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[19] K. Y. Wang, Some researches on fuzzy domains and fuzzy quantales, Ph. D. Thesis, College
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51
ORIGINAL_ARTICLE
L-FUZZY CONVEXITY INDUCED BY L-CONVEX FUZZY SUBLATTICE DEGREE
In this paper, the notion of $L$-convex fuzzy sublattices is introduced and their characterizations are given. Furthermore, the notion of the degree to which an $L$-subset is an $L$-convex fuzzy sublattice is proposed and its some characterizations are given. Besides, the $L$-convex fuzzy sublattice degrees of the homomorphic image and pre-image of an $L$-subset are studied. Finally, we obtain an $L$-fuzzy convexity, which is induced by the $L$-convex fuzzy sublattice degrees, in the sense of Shi and Xiu.
http://ijfs.usb.ac.ir/article_3434_b495c3e9632ab62dfe43bbdb7a7956a4.pdf
2017-10-30T11:23:20
2018-02-25T11:23:20
83
102
10.22111/ijfs.2017.3434
$L$-convex fuzzy sublattice
Implication operator
$L$-convex fuzzy sublattice degree
$L$-fuzzy convexity
Juan
Li
lijuan@htu.edu.cn
true
1
School of Mathematics, Beijing Institute of Technology, Beijing 100081,
PR China
School of Mathematics, Beijing Institute of Technology, Beijing 100081,
PR China
School of Mathematics, Beijing Institute of Technology, Beijing 100081,
PR China
LEAD_AUTHOR
Fu Gui
Shi
true
2
School of Mathematics, Beijing Institute of Technology, Beijing 100081,
PR China
School of Mathematics, Beijing Institute of Technology, Beijing 100081,
PR China
School of Mathematics, Beijing Institute of Technology, Beijing 100081,
PR China
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Press, Cambridge, 1990.
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Information Sciences, 308 (2015), 61{71.
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Sets and Systems, http://dx.doi.org/10.1016/j.fss.2016.02.014.
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13(4) (2016), 51{61.
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Math., 2014 (2014), 12 pages.
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3655-3669.
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Beijing Institute of Technology, (in Chinese), 2015.
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ORIGINAL_ARTICLE
GENERAL FUZZY AUTOMATA BASED ON COMPLETE RESIDUATED LATTICE-VALUED
The present paper has been an attempt to investigate the general fuzzy automata on the basis of complete residuated lattice-valued ($L$-GFAs). The study has been chiefly inspired from the work by Mockor \cite{15, 16, 17}. Regarding this, the categorical issue of $L$-GFAs has been studied in more details. The main issues addressed in this research include: (1) investigating the relationship between the category of $L$-GFAs and the category of non-deterministic automata (NDAs); as well as the relationship between the category of generalized $L$-GFAs and the category of NDAs; (2) demonstrating the existence of isomorphism between the category of $L$-GFAs and the subcategory of generalized $L$-GFAs and between the category of $L$-GFAs and the category of sets of NDAs; (3) and further scrutinizing some specific relationship between the output $L$-valued subsets of generalized $L$-GFAs and the output $L$-valued of NDAs.
http://ijfs.usb.ac.ir/article_3435_d7de60bfd33f9d7c4b580919e282781c.pdf
2017-10-30T11:23:20
2018-02-25T11:23:20
103
121
10.22111/ijfs.2017.3435
General fuzzy automata
Active state set
Residuated-lattice
Isomorphism of category
Functor
K.
Abolpour
true
1
Department of Mathematics, Kazerun Branch, Islamic Azad University, Kazerun, Iran
Department of Mathematics, Kazerun Branch, Islamic Azad University, Kazerun, Iran
Department of Mathematics, Kazerun Branch, Islamic Azad University, Kazerun, Iran
LEAD_AUTHOR
M. M.
Zahedi
zahedi_mm@ mail.uk.ac.ir
true
2
Department of Mathematics, Kerman Graduate University of Advanced Technology, Kerman, Iran
Department of Mathematics, Kerman Graduate University of Advanced Technology, Kerman, Iran
Department of Mathematics, Kerman Graduate University of Advanced Technology, Kerman, Iran
AUTHOR
[1] K. Abolpour and M. M. Zahedi, Isomorphism between two BL-general fuzzy automata, Soft
1
Comput., 16 (2012), 729-736.
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(Ed.), Category Theory Applied to Computation and Control, Proc. First Internat. Symp.
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Amherst MA, 1974, Lecture Notes in Computer Science, Springer, Berlin, 25 (1975), 62-78.
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Proc. First Internat. Symp. Amherst MA, 1974, Lecture Notes in Computer Science, Springer,
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Berlin, 25 (1975), 2-41.
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156 (2006), 855-864.
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valued nite state machines, Information Sciences, 288 (2014), 279-289.
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ing, 47 (2015), 401-416.
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categorical approach, Fuzzy Sets and Systems, 160 (2009), 2416-2428.
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70
ORIGINAL_ARTICLE
SOME COUPLED FIXED POINT RESULTS ON MODIFIED INTUITIONISTIC FUZZY METRIC SPACES AND APPLICATION TO INTEGRAL TYPE CONTRACTION
In this paper, we introduce fruitful concepts of common limit range and joint common limit range for coupled mappings on modified intuitionistic fuzzy metric spaces. An illustrations are also given to justify the notion of common limit range and joint common limit range property for coupled maps. The purpose of this paper is to prove fixed point results for coupled mappings on modified intuitionistic fuzzy metric spaces. Moreover, we extend the notion of common limit range property and E.A property for coupled maps on modified intuitionistic fuzzy metric spaces. As an application, we extend our main result to integral type contraction condition and also for finite number of mappings on modified intuitionistic fuzzy metric spaces.
http://ijfs.usb.ac.ir/article_3436_fff7b56affd28a047b3d02fce1c85f32.pdf
2017-10-30T11:23:20
2018-02-25T11:23:20
123
137
10.22111/ijfs.2017.3436
Modified intuitionistic fuzzy metric space (MIFM-space)
Coupled maps
Common limit range property
Joint common limit range property
E.A property
Weakly compatible mappings
Vishal
Gupta
vishal.gmn@gmail.com
true
1
Department of Mathematics, Maharishi Markandeshwar University,
Mullana-133207, Ambala, Haryana, India
Department of Mathematics, Maharishi Markandeshwar University,
Mullana-133207, Ambala, Haryana, India
Department of Mathematics, Maharishi Markandeshwar University,
Mullana-133207, Ambala, Haryana, India
LEAD_AUTHOR
Rajesh
Kumar Saini
true
2
Department of Mathematics, Statistics and Computer Applications, Bundelkhand University, Jhansi, U.P., India
Department of Mathematics, Statistics and Computer Applications, Bundelkhand University, Jhansi, U.P., India
Department of Mathematics, Statistics and Computer Applications, Bundelkhand University, Jhansi, U.P., India
AUTHOR
Ashima
Kanwar
kanwar.ashima87@gmail.com
true
3
Department of Mathematics, Maharishi Markandeshwar University,
Mullana-133207, Ambala, Haryana, India
Department of Mathematics, Maharishi Markandeshwar University,
Mullana-133207, Ambala, Haryana, India
Department of Mathematics, Maharishi Markandeshwar University,
Mullana-133207, Ambala, Haryana, India
AUTHOR
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1
tractive conditions, J. Math. Anal. Appl., 270 (2002), 181{188.
2
[2] M. Abbas, M. Ali Khan and S. Radenovic, Common coupled xed point theorems in cone
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metric spaces for w-compatible mappings, Appl. Math. Comput., 217(1) (2010), 195{202.
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spaces using JCLR property, Smart Innovation, Systems and Technologies, Springer, 43(1)
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(2016), 201{208.
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, Indian Journal of Science and Technology, 5(12) (2012), 3767{3769.
24
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metric space , Journal of Nonlinear Science and its Applications, 3(2) (2010), 96{109.
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matics, 87(2) (2013), 333{347.
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[16] V. Lakshmikantham and Lj. B. Ciric, Coupled xed point theorems for nonlinear contractions
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in partially ordered metric space , Nonlinear Anal. TMA., 70 (2009), 4341{4329.
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[17] J. H. Park, Intuitionistic fuzzy metric spaces, Chaos, Solitons and Fractals, 22(5) (2004),
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1039{1046.
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Fractals, 27(2) (2006), 331{344.
37
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theorems, Chaos, Solitons and Fractals, 38(1) (2008), 36{47.
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expansion type maps in fuzzy metric space, Thai Journal of Mathematics, 5(2) (2007), 245{
41
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intuitionistic fuzzy metric spaces with common property (E.A.), Fixed Point Theory and
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50
ORIGINAL_ARTICLE
INTERVAL ANALYSIS-BASED HYPERBOX GRANULAR COMPUTING CLASSIFICATION ALGORITHMS
Representation of a granule, relation and operation between two granules are mainly researched in granular computing. Hyperbox granular computing classification algorithms (HBGrC) are proposed based on interval analysis. Firstly, a granule is represented as the hyperbox which is the Cartesian product of $N$ intervals for classification in the $N$-dimensional space. Secondly, the relation between two hyperbox granules is measured by the novel positive valuation function induced by the two endpoints of an interval, where the operations between two hyperbox granules are designed so as to include granules with different granularity. Thirdly, hyperbox granular computing classification algorithms are designed on the basis of the operations between two hyperbox granules, the fuzzy inclusion relation between two hyperbox granules, and the granularity threshold. We demonstrate the superior performance of the proposed algorithms compared with the traditional classification algorithms, such as, Random Forest (RF), Support Vector Machines (SVMs), and Multilayer Perceptron (MLP).
http://ijfs.usb.ac.ir/article_3437_aafac877f889b32648b9815b14c238e2.pdf
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156
10.22111/ijfs.2017.3437
Fuzzy lattice
Granular computing
Hyperbox granule
Fuzzy inclusion relation
Hongbing
Liu
liuhbing@126.com
true
1
Center of Computing, Xinyang Normal University, Xinyang 464000,
P. R. China
Center of Computing, Xinyang Normal University, Xinyang 464000,
P. R. China
Center of Computing, Xinyang Normal University, Xinyang 464000,
P. R. China
LEAD_AUTHOR
Jin
Li
lijin@xynu.edu.cn
true
2
Center of Computing, Xinyang Normal University, Xinyang 464000, P. R. China
Center of Computing, Xinyang Normal University, Xinyang 464000, P. R. China
Center of Computing, Xinyang Normal University, Xinyang 464000, P. R. China
AUTHOR
Huaping
Guo
hpguo_cm@163.com
true
3
School of Computer and Information Technology, Xinyang Normal
University, Xinyang 464000, P. R. China
School of Computer and Information Technology, Xinyang Normal
University, Xinyang 464000, P. R. China
School of Computer and Information Technology, Xinyang Normal
University, Xinyang 464000, P. R. China
AUTHOR
Chunhua
Liu
zzdxliuch@163.com
true
4
School of Computer and Information Technology, Xinyang Normal
University, Xinyang 464000, P. R. China
School of Computer and Information Technology, Xinyang Normal
University, Xinyang 464000, P. R. China
School of Computer and Information Technology, Xinyang Normal
University, Xinyang 464000, P. R. China
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ORIGINAL_ARTICLE
Persian-translation vol. 14, no. 5, October 2017
http://ijfs.usb.ac.ir/article_3439_736c38cb31e5500e70b8efba086f3d16.pdf
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159
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10.22111/ijfs.2017.3439